{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ImageNet 模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.pylab as plt\n",
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub\n",
    "from tensorflow.keras import layers, datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url =\"https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4\"\n",
    "model = tf.keras.Sequential([\n",
    "    hub.KerasLayer(url, input_shape=(224, 224, 3))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x14220F400>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tutu = tf.keras.utils.get_file('tutu.png','https://geektutu.com/img/icon.png')\n",
    "tutu = Image.open(tutu).resize((224, 224))\n",
    "tutu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "result.shape: (1, 1001) ans: 332\n"
     ]
    }
   ],
   "source": [
    "result = model.predict(np.array(tutu).reshape(1, 224, 224, 3)/255.0)\n",
    "ans = np.argmax(result[0], axis=-1)\n",
    "print('result.shape:', result.shape, 'ans:', ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hare\n"
     ]
    }
   ],
   "source": [
    "labels_url = 'https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt'\n",
    "labels_path = tf.keras.utils.get_file('ImageNetLabels.txt', labels_url)\n",
    "imagenet_labels = np.array(open(labels_path).read().splitlines())\n",
    "print(imagenet_labels[ans])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 迁移学习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### resize 数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resize(d, size=(224, 224)):\n",
    "    return np.array([np.array(Image.fromarray(v).resize(size, Image.ANTIALIAS))\n",
    "                     for i, v in enumerate(d)])\n",
    "\n",
    "(train_x, train_y), (test_x, test_y) = datasets.cifar10.load_data()\n",
    "train_x, test_x = resize(train_x[:30000])/255.0, resize(test_x)/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y = train_y[:30000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 下载特征提取层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_extractor_url = 'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4'\n",
    "feature_extractor_layer = hub.KerasLayer(feature_extractor_url,\n",
    "                                         input_shape=(224,224,3))\n",
    "# 这一层的训练值保持不变\n",
    "feature_extractor_layer.trainable = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 添加分类层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "keras_layer_1 (KerasLayer)   (None, 1280)              2257984   \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 10)                12810     \n",
      "=================================================================\n",
      "Total params: 2,270,794\n",
      "Trainable params: 12,810\n",
      "Non-trainable params: 2,257,984\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    feature_extractor_layer,\n",
    "    layers.Dense(10, activation='softmax')\n",
    "])\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['acc'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练并评估模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0720 22:12:00.175307 140735530943360 deprecation.py:323] From /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 30000 samples\n",
      "30000/30000 [==============================] - 1457s 49ms/sample - loss: 0.9604 - acc: 0.7057\n",
      "10000/10000 [==============================] - 256s 26ms/sample - loss: 0.7636 - acc: 0.7657\n",
      "0.7657\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_x, train_y, epochs=1)\n",
    "loss, acc = model.evaluate(test_x, test_y)\n",
    "print(acc)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
